Machine Learning (ML) can be used in renewable energy systems, such as, for example, wind, tidal, or photovoltaic power systems, to improve the use of variable renewable resources and energy generation and consumption demands. In machine learning models, statistically significant inputs and stochastic methods are used to predict future resource availability and demand requirements, which can then be used to schedule generation, storage, load shaping and pricing to optimize the economics of energy systems including energy grids. The generation of prediction and optimization models can similarly be based on machine learning and can be performed in and are affected by the context of a particular energy grid deployment.
One of the challenges of applying machine learning to systems with data collected over a significant period of time, such as renewable energy systems, is the time required for the model to learn, or be trained. This training requires data to be collected over a sufficiently long time-interval for such machine learning models to be properly trained. Accordingly, critical issues remain with regards to the time required to train and deploy machine learning models for use in systems with time series data, including in energy systems that form an energy grid.
In addition, current machine learning applications do not address the problem of how to use a machine learning model generated in one context to improve the accuracy and reduce deployment time of a machine learning model to be used in another context. For example, to provide better energy generation forecasts in a renewable wind farm, a ML system may use weather predictions and wind turbine system characteristics, such as location of wind turbines, terrain type in which the turbines are located, and proximity to bodies of water, to generate a machine learning model. Similarly, in a photovoltaic power generation system, a ML system may use, for example, weather predictions, locational solar characteristics, and photovoltaic panel and tilt mechanism characteristics, to generate a machine learning model. The sum of this input data is the context within which the machine learning model is generated. But current ML models are typically specific to the context in which they are generated and cannot accurately be used in a different context, e.g., a different renewable wind farm.